Management of Chronic Myeloid Leukemia with Sever COVID 19: A Case Report

Coronavirus disease-19 (COVID-19) is a pandemic viral disease that can cause devastating complications such as acute respiratory disease, especially in patients with comorbidities. We do not know yet full pictures of this disease, especially in hematological malignancies. Here, we present management of a 57-year-old male with chronic phase chronic myeloid leukemia, tested positive for COVID-19, then complicated with acute respiratory distress syndrome

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

A probabilistic approach for failure localization

This work considers the problem of fault localization in transparent optical networks. The aim is to localize single-link failures by utilizing statistical machine learning techniques trained on data that describe the network state upon current and past failure incidents. In particular, a Gaussian Process (GP) classifier is trained on historical data extracted from the examined network, with the goal of modeling and predicting the failure probability of each link therein. To limit the set of suspect links for every failure incident, the proposed approach is complemented with the utilization of a Graph-Based Correlation heuristic. The proposed approach is tested on a dataset generated for an OFDM-based optical network, demonstrating that it achieves a high localization accuracy. The proposed scheme can be used by service providers for reducing the Mean-Time-To-Repair of the failure

Performance analysis of a data-driven quality-of-transmission decision approach on a dynamic multicast- capable metro optical network

The performance of a data-driven qualityof- transmission (QoT) model is investigated on a dynamic metro optical network capable of supporting both unicast and multicast connections. The data-driven QoT technique analyzes data of previous connection requests and, through a training procedure that is performed on a neural network, returns a data-driven QoT model that nearaccurately decides the QoT of the newly arriving requests. The advantages of the data-driven QoT approach over the existing Q-factor techniques are that it is self-adaptive, it is a function of data that are independent from the physical layer impairments (PLIs) eliminating the requirement of specific measurement equipment, and it does not assume the existence of a system with extensive processingandstorage capabilities. Further, it is fast in processing new data and fast in finding a near-accurateQoT model provided that such a model exists. On the contrary, existing Q-factor models lack self-adaptiveness; they are a function of the PLIs, and their evaluation requires time-consuming simulations, lab experiments, specific measurement equipment, and considerable human effort. It is shown that the data-driven QoT model exhibits a high accuracy (close to 92%-95%) in determining, during the provisioning phase, whether a connection to be established has a sufficient (or insufficient) QoT, when compared with the QoT decisions performed by the Q-factor model. It is also shown that, when sufficient wavelength capacity is available in the network, the network performance is not significantly affected when the data-driven QoT model is used for the dynamic system instead of the Q-factor model, which is an indicator that the proposed approach can efficiently replace the existing Q-factor model

A data-driven QoT decision approach for multicast connections in metro optical networks

A data-driven technique for analyzing Quality-of-Transmission (QoT) data of previously established connections is proposed for accurately deciding the QoT of the newly arriving multicast requests in metro optical networks. The proposed approach is self-adaptive, it is a function of data that are independent from the physical layer impairment (PLIs) and thus does not require specific measurement equipment, and it does not assume the existence of a system with extensive processing and storage capabilities. It is also fast in processing new data, and fast in finding a near-accurate QoT model provided that such a model exists. The proposed technique can replace the existing Q-factor models that are not self-adaptive, they are a function of the PLIs, and their evaluation requires time-consuming simulations, lab experiments, specific measurement equipment, and considerable human effort. The proposed data-driven QoT approach is based on the utilization of a feed-forward neural network that is trained on a dataset previously generated from a known Q-factor model. The dataset fed to the neural network is represented in a way that specifically describes the QoT of the multicast connections requesting to be established in the network but it is independent from the PLIs. The validity of the proposed approach is examined for two distinct networks, exhibiting a high accuracy when compared to the results of the Q-factor model utilized for generating the QoT data

Leveraging Statistical Machine Learning to Address Failure Localization in Optical Networks

In this work we consider the problem of fault localization in transparent optical networks. We attempt to localize single-link failures by utilizing statistical machine learning techniques trained on data that describe the network state upon current and past failure incidents. In particular, a Gaussian process classifier is trained on historical data extracted from the examined network, with the goal of modeling and predicting the failure probability of each link therein. To limit the set of suspect links for every failure incident, the proposed approach is complemented by the utilization of a graph-based correlation heuristic. The proposed approach is tested on a number of datasets generated for an orthogonal frequency-division multiplexing-based optical network, and demonstrates that the approach achieves a high localization accuracy (91%-99%) that is insignificantly affected as the size of the historical dataset is reduced. The approach is also compared to a conventional fault localization method that is based on the utilization of monitoring information. It is shown that the conventional method significantly increases the network cost, as measured by the number of monitoring nodes required to achieve the same accuracy as that achieved by the proposed approach. The proposed scheme can be used by service providers to reduce the network cost related to the fault localization procedure. As the approach is generic and does not depend on specific network technologies, it can be applied to different network types, e.g., fixed-grid or space-division multiplexing elastic optical networks

Performance Analysis of a Data-Driven QoT Decision Approach on a Dynamic Multicast-Capable Metro Optical Network

The performance of a data-driven quality-of-transmission (QoT) model is investigated on a dynamic metro optical network capable of supporting both unicast and multicast connections. The data-driven QoT technique analyzes data of previous connection requests and, through a training procedure that is performed on a neural network, returns a data-driven QoT model that nearaccurately decides the QoT of the newly arriving requests. The advantages of the data-driven QoT approach over the existing Q-factor techniques are that it is self-adaptive, it is a function of data that are independent from the physical layer impairments (PLIs) eliminating the requirement of specific measurement equipment, and it does not assume the existence of a system with extensive processingandstorage capabilities. Further, it is fast in processing new data and fast in finding a near-accurateQoT model provided that such a model exists. On the contrary, existing Q-factor models lack self-adaptiveness; they are a function of the PLIs, and their evaluation requires time-consuming simulations, lab experiments, specific measurement equipment, and considerable human effort. It is shown that the data-driven QoT model exhibits a high accuracy (close to 92%–95%) in determining, during the provisioning phase, whether a connection to be established has a sufficient (or insufficient) QoT, when compared with the QoT decisions performed by the Q-factor model. It is also shown that, when sufficient wavelength capacity is available in the network, the network performance is not significantly affected when the data-driven QoT model is used for the dynamic system instead of the Q-factor model, which is an indicator that the proposed approach can efficiently replace the existing Q-factor model